Song, X., Gaurav, A., Pillai, P.B. et al. (5 more authors) (2023) Reservoir computing based on a solid electrolyte ZnO TFT: an attractive platform for flexible edge computing. In: 2023 IEEE International Flexible Electronics Technology Conference (IFETC). 5th IEEE International Flexible Electronics Technology Conference (IFETC) 2023, 13-16 Aug 2023, San Jose, USA. Institute of Electrical and Electronics Engineers , pp. 1-3. ISBN 979-8-3503-3209-4
Abstract
Implementation of accurate neural network models in edge applications such as wearables is limited by the hardware platform due to constraints of power/area. We highlight novel concepts in reservoir computing that rely on a volatile three terminal solid electrolyte thin film synaptic transistor, whose conductance can be controlled by the gate and drain voltages to enhance the richness of the reservoir and operate in the off-state. The proposed approach achieves an accuracy of 94% in image processing, significantly higher than equivalent applications of reservoir computing based on two-Terminal memristors, primarily because we avoid down-sampling by training the readout after every pulse.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 The Authors. Except as otherwise noted, this author-accepted version of a paper published in 2023 IEEE International Flexible Electronics Technology Conference (IFETC) is made available via the University of Sheffield Research Publications and Copyright Policy under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ |
Keywords: | reservoir computing; Solid electrolyte FET; ZnO/Ta2O5) |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Electronic and Electrical Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 26 Feb 2024 15:14 |
Last Modified: | 26 Feb 2024 15:14 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers |
Refereed: | Yes |
Identification Number: | 10.1109/ifetc57334.2023.10254868 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:209583 |
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Licence: CC-BY 4.0